Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Powiadomienia systemowe

Sesja wygasła!
Sesja wygasła!

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: thyroid disease diagnosis

Sortuj według:

Ogranicz wyniki do:

A Transparent AI-Driven Multiclass Decision Support System for Thyroid Risk Prediction Using Machine Learning and Deep Learning Approaches

Ouartani Siouar, Taleb Nora

Foundations of Computing and Decision Sciences

2025

Vol. 50, No. 4

473--509

Early and accurate diagnosis of thyroid disorders is essential due to their prevalence and health impact. To enhance interpretability in clinical settings, we propose a comprehensive workflow for transparent thyroid disease prediction using a multiclass classification problem with five diagnostic categories. A dataset of 9172 samples with 31 features was used to train various machine and deep learning models. A dual-layered framework combining Feature Selection (ETC, MI, RFE) and Explainable AI (SHAP, LIME) improved performance and transparency. Gradient Boosting achieved the highest accuracy (0.97). SHAP explained global feature influence, while LIME clarified individual predictions. Our approach supports interpretable, reliable AI-based diagnostic tools for thyroid disorder classification.

Boosting, bagging and fixed fusion methods performance for aiding diagnosis

Ćwiklińska-Jurkowska M.

Biocybernetics and Biomedical Engineering

2012

Vol. 32, no. 2

17-31

Multiple classifier fusion may generate more accurate classification than each of the constituent classifiers. The aim was to examine the ensemble performance by the comparison of boosting, bagging and fixed fusion methods for aiding diagnosis. Real-life medical data set for thyroid diseases recognition was applied. Different fixed combined classifiers (mean, average, product, minimum, maximum, and majority vote) built on parametric and nonparametric Bayesian discriminant methods have been employed. No very significant improvement of recognition rates by a fixed classifier combination was achieved on the examined data. The best performance was obtained for resampling methods with classification trees, for both the bagging and the boosting combining methods. The bagging and the boosting logistic regression methods have proven less efficient than the bagging or the boosting of neural networks. Difference between the bagging and the boosting performance for the examined data set was not obtained.